An integrated feedforward-feedback control structure utilizing a simplified global gravitational search algorithm to con
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Sådhanå (2020)45:252 https://doi.org/10.1007/s12046-020-01491-2
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An integrated feedforward-feedback control structure utilizing a simplified global gravitational search algorithm to control nonlinear systems OMAR FAROUQ LUTFY Control and Systems Engineering Department, University of Technology, Baghdad, Iraq e-mail: [email protected] MS received 15 May 2018; revised 18 April 2020; accepted 8 September 2020 Abstract. This paper presents an integrated feedforward-feedback control structure to control nonlinear dynamical systems. This intelligent control system exploits a modified recurrent wavelet neural network (MRWNN) in the feedforward (FF) and the feedback (FB) loops of the control structure. Specifically, the MRWNN is proposed to boost the approximation performance of a previously reported network by employing two amendments to the original structure. To optimize the parameters of both the FF and the FB controllers, an enhanced version of the gravitational search algorithm (GSA) is developed to improve the searching capability of the original algorithm. In particular, two modifications were adopted, including the removal of two control parameters related to the gravitational constant in the original algorithm and the utilization of the global best solution to constitute the next generation of agents. Hence, the proposed algorithm is called the simplified global gravitational search algorithm (SGGSA), which has demonstrated better optimization performance compared to those of other techniques, including the original GSA. By conducting several evaluation tests using different nonlinear time-variant dynamical systems, the effectiveness of the proposed control structure was confirmed in terms of control precision and robustness against external disturbances. In addition, the MRWNN has exhibited a superior control performance compared with other related controllers. Keywords. Integrated feedforward-feedback control structure; wavelet neural network; recurrent wavelet neural network; gravitational search algorithm; genetic algorithm.
1. Introduction The increased complexity of modern industrial systems has rendered linear modeling and control techniques insufficient tools that will inevitably result in poor control performance. The reason behind this performance degradation in linear control techniques can be attributed to the description of the complex and nonlinear physical system behavior by just an approximated linear model [1]. To tackle this difficulty, more attention has been paid to utilize intelligent control methods that can be directly applied to handle the complexity and nonlinearity of the systems. In particular, artificial intelligence techniques can be combined with conventional control schemes to form efficient nonlinear control systems. To this end, among several control schemes, the feedback (FB) and the feedforward (FF) strategies are the most commonly applied schemes due to their simple configuration and good control performance. Howe
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